For the modern enterprise, maintaining data consistency with respect to data originating from a variety of data sources is strategically important to the enterprise. This requirement may be achieved by implementing a data warehouse. To that end, SAP's Business Information Warehouse (BW) system consolidates data (e.g., external and the internal sources of data) into a single repository. Moreover, the BW provides preconfigured data and methods to aid a business enterprise when dealing with data management and archiving.
One aspect of the BW is the cube (also referred to as an infocube). The cube refers to multidimensional data. In some cases, the cube is multi-dimensional data physically represented as a “star” schema, a “snowflake” schema, or some other type of structure as well. Cubes thus provide multidimensional data storage containers for reporting data and for analyzing data. The star schema refers to a structure of data including one or more fact tables and one or more dimension tables. The facts of the fact table are classified along the dimensions. For example, the fact tables hold the main data, while the dimension tables describe dimension data (typically referred to as characteristics) that can be joined to fact tables as needed. The snowflake schema is another way of arranging fact and dimension tables in a relational database such that the entity relationship diagram resembles a snowflake in shape. At the center of the snowflake schema are one or more fact tables, which are connected to multiple dimension group tables, each grouping together several dimensions. The star and snowflake schemas thus provide ways to implement a multi-dimensional database using a mainstream relational database.
FIG. 4 depicts an example framework for a cube and, more specifically, a snowflake schema. The cube is a database framework (or architecture) including a central database table, such as a fact table 410. The fact table may include measures (also typically referred to as key figures) corresponding to data of interest. The measures are data that can be aggregated (e.g., added). The fact table may be surrounded by associated dimension group tables, such as dimension group table 420, that group one or more other dimensions, such as characteristic tables. The dimension group tables include references pointing to master data tables including so-called characteristics assigned to the measures. A dimension group table may be used as a simple grouping of characteristics that do not necessarily have hierarchical dependencies. For example, characteristics that logically belong together (district and area, for example, belong to a regional dimension) may be grouped together in a dimension group table. By adhering to this design criterion, dimension group tables are largely independent of each other, and these dimension group tables remain small with regards to data volume, which may be desirable for reasons of performance. The dimension group table offers the advantage of a fewer number of indexes required in the fact tables compared to a star scheme not using dimension group tables.